Rethinking the

Cross-Chain Swap Experience

Redesigned a legacy swap feature to enable seamless cross-chain transactions within a multi-asset crypto wallet. With AI-assisted research and analysis, the team simplified complexity, improved reliability, and built user trust—particularly for non-expert crypto users during high-stress transactions.

My Role: Design Lead / UX Designer
Duration: 6 months Team: 5 designers
Other Teams: Product, DEVs, Business, Data, CRM

01. Impact


  • Improved clarity and trust in the swap process

  • Reduced user friction across key flows

  • Established a repeatable design–product collaboration framework later applied to other features.

02. Overview


Challenge

The existing swap feature only supported same-chain transactions and had an outdated design. The goal was to introduce cross-chain swaps through a third-party SDK while ensuring a frictionless, intuitive experience that concealed technical complexity from the user.

Objectives

Enable cross-chain swaps without leaving the app

Deliver a seamless flow between native and third-party experiences

Simplify choices and language for non-expert crypto users

Improve clarity, feedback, and reassurance during transaction tracking

Ensure scalability for future updates

03. Research & Competitive Analyses


Benchmark

We tested and analysed leading wallets and DeFi apps to evaluate:

Information architecture and flow structure

Empty states and
edge-case handling

Review and tracking screens

Notification patterns and success feedback

To accelerate this process, we used AI to cluster UX patterns and summarize insights across competitors.

This helped us spot what worked, what didn’t, and how we could simplify things for our users.

Findings guided the UX direction: we didn’t want to overwhelmed users with technical options (provider selection, gas management, advanced settings).

Our approach prioritised clarity over control, with contextual help and minimal decisions.

Part of the benchmark and ideas that guided next steps

04. Defining the User Journey


Based on the product’s technical brief, we created frontstage/backstage journey maps showing every step between the user interface and background blockchain processes.

Technical journey map, highlighting front and backstage steps

This helped us:

  • Identify dependencies, blockers,
    and potential wait times

  • Define when and how to surface progress and error feedback

  • Simplify the visible flow while maintaining backend awareness

05. UX Design & Feature Definition


We collaborated closely with product and business stakeholders to prioritise features.
After mapping third party flows, we simplified the architecture, made task flows, reduced screens, and rewrote copy for accessibility. 

The goal: make complex blockchain actions feel simple, predictable, and safe.

Initial flows based on third party solutions

Wireframe iterations covering some key stages of the experience.

06. Testing & Validation


We performed several User interviews to validate design assumptions:

Users appreciated simplicity and automation

Users wanted visible progress tracking (“reassurance moments”)

Tooltips and settings were under-discovered

Some steps (review & progress) still felt overwhelming

AI transcription and summarization helped uncover friction points and guided UX tweaks, making flows clearer, faster, and easier to use.

07. Delivery & Handoff


Once approved, the designs went through an engineer review to validate feasibility and interaction logic before handoff. We then finalised components and delivered production-ready files with complete implementation documentation, including:

A handoff file detailing happy paths, edge cases, and failure states.

Screen-level interaction notes outlining default states and available actions.

Examples of handoff documentation

08. Design QA


Before release, we ran a full Design QA (Quality Assessment) to ensure implementation accuracy. Each issue was categorised by impact:

High

Blocks user flow

Medium

UX deviation, recoverable

Low

Visual inconsistency

This systematic QA reinforced the product’s design quality and ensured parity between the final build and the intended user experience.

Example of design QA feedback, comparing the final design files with the staging implementation.

09. Post-Launch Behavioural Insights


After launch, we looked at customer feedback, behavioural analytics, and session replays to see how people really used the feature. AI tools helped us spot patterns and correlations. These insights revealed key friction points and guided targeted UX refinements — improving clarity, performance, and overall flow efficiency.

Session replay analysis, observation notes and a presentation of insights to the team.

10. AI-Assisted Design Process


AI tools played a supporting role across multiple stages of this project — accelerating insight gathering and decision-making:

  • Research: Automated clustering and pattern recognition helped identify shared UX models across leading crypto wallets.

  • User Interviews: AI transcription and summarization simplified qualitative analysis, allowing the team to map themes and sentiment faster.

  • Behavioural Analysis: Post-launch, AI-driven analytics surfaced hidden correlations between friction points and task completion, guiding targeted improvements.

By integrating AI into the workflow, the design team was able to focus on strategic thinking and creative problem-solving, rather than manual data synthesis.

11. Conclusions


This project demonstrated how design bridges user needs, technical feasibility, and business goals. By combining real user feedback with close cross-team collaboration, we created a solution that simplified complex blockchain interactions and aligned stakeholders around a shared, user-centered vision. It also established a repeatable framework for validation and post-launch analysis — reinforcing how strategic design drives innovation, usability, and long-term value.

Simple design is not about fewer features, it’s about fewer doubts.

Next steps

The next step involved user interviews to uncover the “why” behind user behavior — insights that would inform a more intuitive, data-driven redesign of the swap experience.

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